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Esfahan, Iran

The Malek-Ashtar University of Technology is a university of engineering, science, and military in Iran. This university was opened in 1986. Its campuses are located in Tehran and Isfahan and Urmia. The university is named after Malik al-Ashtar,one of the most loyal companions of Ali Ibn Abi Talib. Malek-Ashtar University of Technology is often referred to "MUT" by the abbreviation.The university does not provide free access and is not open to visitors. Visitors can only enter the university after getting permission from the person they want to visit. Their identification will be registered at the entrance and they should give a valid identification card that can be collected at the exit.Malek-Ashtar University of Technology provides both undergraduate and graduate programs. Funding for Malek-Ashtar University of Technology is provided by the Ministry of Defense. All the academic staff are employees of the Iranian Ministry of Defense and they cannot leave Iran without permission. Admission is done by the national entrance examination administered yearly by the Ministry of Science, Research and Technology.In 2005, Malek-Ashtar University of Technology was identified by the German government as an industry in a mainly civilian institution which also conducts military research and development. In June 2008, Malek-Ashtar University was listed by the European Union as an entity linked to Iran's proliferation-sensitive nuclear activities or Iran's development of nuclear weapon delivery systems. Within their jurisdiction, European Union members must block all funds or economic resources of the listed entity. Wikipedia.


Keshavarz M.H.,Malek-Ashtar University of Technology
Journal of Hazardous Materials | Year: 2010

A simple method is used to predict heats of sublimation of energetic compounds, which include nitroaromatics, nitramines, nitroaliphatics and nitrate esters. Molecular weight, some specific functional groups and structural parameters are fundamental factors in the new model. The root-mean-square deviation (rms) from experiment has been calculated for the predicted results of 92 different compounds. The calculated results for 15 compounds are also compared with complicated quantum mechanical computations, where computed outputs were available. The rms deviations of new correlation and reported quantum mechanical method are 9.9 and 13.8kJ/mol, respectively. The present method improves earlier efforts of previous models through its application for important classes of energetic compounds, which contain the energetic bonds Ar-NO 2, N-NO 2, C-NO 2 and C-O-NO 2. © 2009 Elsevier B.V. Source


Sheikh H.,Malek-Ashtar University of Technology
Scripta Materialia | Year: 2011

The hot accumulative roll bonding (ARB) process was applied on sheets of an Al-Mg alloy for up to six cycles. The electron backscattering diffraction (EBSD) method was employed to investigate the microtextural aspects of the starting material and the ARBed samples. The results indicate that the presence of shear bands changes the components of the microtexture. At the fourth and sixth cycles, the overall microtexture intensity increases and the main microtextural components are copper, Dillamore, Goss, and brass. © 2010 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved. Source


Ghasemi A.,Malek-Ashtar University of Technology
Journal of Magnetism and Magnetic Materials | Year: 2012

In this research work, magnetic multi-walled carbon nanotube (MWCNTs) nanocomposites have been created by the assembly of MgCoZr substituted barium ferrite film onto the surface of MWCNTs. Microwave absorption of the MWCNTs/doped barium ferrite nanocomposites is evidently enhanced compared to that of pure MWCNTs and substituted ferrites. The maximum reflection loss increased significantly with an increase in volume percentage of MWCNTs. Reflection loss evaluations indicated that nanocomposites display a great potential application as thinner and lighter wide-band electromagnetic wave absorbers. © 2011 Elsevier B.V. All rights reserved. Source


Ghasemi A.,Malek-Ashtar University of Technology
Journal of Magnetism and Magnetic Materials | Year: 2011

In this research work, magnetic multi-walled carbon nanotube (MWCNT) nanocomposites have been created by the assembly of ZnSn substituted strontium ferrite film onto the surface of MWCNTs. X-ray diffraction and transmission electron microscopy were used to demonstrate the successful attachment of ferrite films to MWCNTs. Mössbauer spectroscopy indicates that the ZnSn ions preferentially occupy the 2b and 4f 2 sites. Vibrating sample magnetometry confirms the relatively strong dependence of saturation magnetization with the volume percentage of MWCNTs. Microwave absorption of the MWCNTs/doped strontium ferrite nanocomposites is evidently enhanced compared to that of pure MWCNTs and ferrite. The maximum reflection loss increased significantly with an increase in volume percentage of MWCNTs in nanocomposites. Reflection loss evaluations indicated that the nanocomposites display a great potential application as wide-band electromagnetic wave absorbers. © 2011 Elsevier B.V. All rights reserved. Source


Keshavarz M.H.,Malek-Ashtar University of Technology
Propellants, Explosives, Pyrotechnics | Year: 2013

This paper describes an improved simple model for prediction of impact sensitivity of different classes of energetic compounds containing nitropyridines, nitroimidazoles, nitropyrazoles, nitrofurazanes, nitrotriazoles, nitropyrimidines, polynitroarenes, benzofuroxans, polynitroarenes with α-CH, nitramines, nitroaliphatics, nitroaliphatic containing other functional groups, and nitrate energetic compounds. The model is based on some molecular structural parameters. It is applied for 90 explosives, which have different molecular structures. The predicted results are compared with outputs of complex neural network approach as one of the best available methods. Root mean squares (rms) of deviations of different energetic compounds are 24 and 49 cm, corresponding to 5.88 and 12.01 J with 2.5 kg dropping mass, for new and neural network methods, respectively. The novel model also predicts good results for eight new synthesized and miscellaneous explosives with respect to experimental data. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. Source

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